Hello,
I have been following some of the tutorial provided by hbc training specifically on integrating different datasets: https://hbctraining.github.io/scRNA-seq/lessons/06_SC_SCT_and_integration.html
I believe I have encountered a slight issue. I followed much of the code that was given on the page; I had all of samples in one seuratobject and I split them then performed SCtransformation on EACH SEPARATELY(NOTE I didn;t regress out cell cylce):
split_srt <- SplitObject(sample.merge, split.by = "Sample.Name")
for (i in 1:length(split_srt)) {
split_srt[[i]] <- NormalizeData(split_srt[[i]], verbose = TRUE)
split_srt[[i]] <- SCTransform(split_srt[[i]], vars.to.regress = c("percent.MT"))
}
I then performed the suggested integration steps:
integ_features <- SelectIntegrationFeatures(object.list = split_srt,
nfeatures = 3000)
split_srt <- PrepSCTIntegration(object.list = split_srt,
anchor.features = integ_features)
integ_anchors <- FindIntegrationAnchors(object.list = split_srt,
normalization.method = "SCT",
anchor.features = integ_features)
seurat_integrated <- IntegrateData(anchorset = integ_anchors,
normalization.method = "SCT")
Running a PCA and TSNE yield dimensionality reduction that looked quite integrated:

But the issue is when I try to find marker genes, it appears that expression of most genes is seen as background; IE there are no white dots on a featureplot:
seurat_integrated <- FindNeighbors(seurat_integrated,dims = 1:30)
seurat_integrated <- FindClusters(seurat_integrated, resolution = 0.5)
Merged.markers <- FindAllMarkers(seurat_integrated, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
plotting some of these markers


I am slightly unsure what I have done wrong/if I missed some steps. I would greatly appreciate any help I get.